3 papers
cs.LG2025
Scalable Bayesian Learning with posteriors
Samuel Duffield, Kaelan Donatella, Johnathan Chiu +2
Although theoretically compelling, Bayesian learning with modern machine learning models is computationally challenging since it requires approximating a high dimensional posterior…
cs.ET2025
Scalable Thermodynamic Second-order Optimization
Kaelan Donatella, Samuel Duffield, Denis Melanson +7
Many hardware proposals have aimed to accelerate inference in AI workloads. Less attention has been paid to hardware acceleration of training, despite the enormous societal impact…
cond-mat.stat-mech2024
Thermodynamic Bayesian Inference
Maxwell Aifer, Samuel Duffield, Kaelan Donatella +6
A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks…